Accurate and efficient galaxy classification based on mobile vision transformer

Abstract

Understanding the formation and evolution of galaxies in observational cosmology heavily relies on galaxy morphological classification. Nevertheless, the continuously growing volume of astronomical data has surpassed human capacity for manual classification. In this context, deep learning presents a promising approach to enhancing classifying galaxies. In this paper, the Mobile Vision Transformer (MobileViT) is introduced to construct an efficient and accurate galaxy classifier. Transfer learning is introduced to assist in model fine-tuning. MobileViT combines the features of MobileNet and Visual Transformer (ViT). A lightweight model is used to effectively analyse the relationships between sequences for efficient and accurate classification. Experiments are built on Galaxy10 DECals dataset. Excellent performance is achieved in identifying galaxy types compared to other lightweight models. The model achieves an accuracy of over 87% and maintains a high speed of inference of less than 50 milliseconds per step. Experimental results show that the introduction of MobileViT is the best solution for efficient galaxy classification. The model can be deployed on any portable device for instant observation and classification.